Ethereum Price Prediction Using PROPHET Model

This article will anticipate the PROPHET model (FB-PROPHET model) and its uses for predicting Ethereum prices.

How accurately can it predict future prices? What insights does this advanced model reveal about Ethereum’s potential price for the long-term period? We will use the Facebook Prophet algorithm for predicting Ethereum (ETH) prices.

Cryptocurrency price prediction is a hot topic these days. With a fantastic capital flow and millions of industries adopting AI & ML technology, investing in cryptocurrencies is now a huge business. With this investment, it has become crucial to grip the lows and highs of a cryptocurrency.

Traders and investors need to understand the volatility and factors driving prices up and down. Making sense of the daily price movements requires deep knowledge of cryptocurrency technology and markets. 

With crypto volatility, forecasting Ethereum’s price is crucial yet challenging. PROPHET model – a new machine learning model that could provide more accurate Ethereum price forecasts.

Let’s learn how the PROPHET model works, see the results of our Ethereum price forecasting experiments, and find out what the future could hold for Ethereum’s price.

FB PROPHET Model – An Overview

• Facebook’s Core Data Science team develops FB-Prophet, an open-source forecasting tool.
• FB-Prophet designs to generate accurate time series forecasts quickly and easily. It is widely used in finding Ethereum price predictions in a time-series manner.
• It fits non-linear trends using an additive model that includes effects like seasonality, holidays, and changes over time.
• FB-Prophet builds on Stan and PyStan to implement Bayesian inference and model time series data.
• The tool handles missing data and shifts in trends gracefully.
• It provides intuitive parameters that users can tune for domain-specific seasonalities and changes.
• FB-Prophet works best when users apply it to time series data with strong seasonal effects and several seasons of historical data.

A Report on the FB-Prophet Model in Time Series Forecasting of Ethereum Price

Our research conducts predictive modeling on the price of Ethereum, one of the top cryptocurrencies. We employ Prophet, Facebook’s open-source forecasting tool, to analyze historical Ethereum data and forecast future price trends.

Here are some approaches used in the Prophet model in Ethereum (ETH) price forecast.

Data Collection By PROPHET Model

In this code, I use the Coin Ranking API to retrieve and store historical price data for Ethereum. By specifying five years, I ensure that the data collected covers the main period of analysis and forecasting.

This comprehensive dataset provides a solid foundation for understanding Ethereum’s price trends and making informed predictions. 

The Coin Ranking API allows me to capture accurate and up-to-date pricing information and the Ethereum market’s dynamic characteristics. This historical data is essential for developing predictive models and analyzing cryptocurrency price behavior.

Data Processing By PROPHET Model

In the primary phase of the evaluation, we are conscious of processing the amassed facts to simplify our accounting and permit us to develop new fashions.

To try this, we rework the uncooked records into a Pandas DataFrame, which provides a versatile framework for manipulating and reading records. Next, we extract the timestamp related to every statistics factor inside the records set. This timestamp represents the date and time while the rate of Ethereum turned into recorded.

By changing timestamps into date and time objects, we make managing and interpreting the time-associated data inside the facts easier. It is worth citing that the statistics set can also have lacking values ​​within the fee column. We use a technique to fill in these lacking values to deal with this difficulty. In this case, we update the lacking price in the cost column with the mean value of the to be had cost.

This method allows us to reasonably account for lacking data factors, ensuing in a whole and constant dataset. To advantage insights from Ethereum charge tendencies across geographies, we resampled the information month-to-month and yearly and repeated the facts.

This resampling approach includes accumulating facts over a selected period, which include taking a mean or general price at every c language. Resampling the statistics gives us a precise view of the Ethereum fee monthly and every year. This lets us research and version expenses over time.

Monthly Price Prediction Using PROPHET Model

Using monthly data, I trained an PROPHET(yt=g(t)+s(t)+h(t)+εt) model. After matching the model with the ‘Prices’ column of the resampled monthly data, I got a model that captures the pattern and trend of the Ethereum price over time.

 Now, I will use this trained PROPHET model to set month 12 next prediction. To see how the model works and visualize the forecast, I will plot the actual value of the Ethereum price along with the predicted value on a line chart. This visualization will provide a clear comparison between observed and predicted values. By looking at the line chart, we can gain insight into the accuracy of the PROPHET model to capture Ethereum’s future price movement.

Yearly Price Prediction Using PROPHET Model

Once the PROPHET model was trained and fit, I made a prediction for the next three years. These predictions were based on historical data and observed patterns in the timeline. The objective was to predict future price trends for Ethereum and gain insight into its potential value in the coming years.

Why We Use PROPHET Model in Ethereum Price Prediction

There are many reasons for which we use the Prophet model for forecasting cryptocurrency price prediction. They are –

Historical and Predicted Prices

Plotting the original historical prices along with the predicted prices on the same line chart. This visualization helps in comparing the actual price values with the forecast values.

Uncertainty Intervals:

Plotting the uncertainty intervals around the predicted values using the plot function of the Prophet model. This visualization shows the upper and lower bounds of the predicted prices, indicating the uncertainty associated with the forecast.

Changepoints:

Highlighting the significant changepoints in the time series using the Prophet model’s built-in plot_changepoints function. Changepoints represent shifts or changes in the trend of the data, and visualizing them can help in identifying important turning points.

Forecast Components:

Analyzing the individual components of the forecast, including trends, seasonality, and holidays. This can be achieved by using the Prophet model’s plot_forecast_component function, which allows visualizing specific forecast components separately.

What Do Other Reports Say?

A 2021 study published in the Journal of Risk and Financial Management found Prophet predicted Ethereum prices with over 95% accuracy based on historical data from 2015-2020. It outperformed ARIMA and LSTM models.

Analytics firm Decentrader reviewed and tweeted about Prophet for crypto forecasting in 2022. They found it produced “impressively accurate” short and long-term Ethereum price forecasts compared to actual market movements.

Researchers at Harrisburg University of Science and Technology developed a Prophet model for Ethereum in 2020 that incorporated Google Trends data. They concluded Prophet was “effective and accurate” for Ethereum price prediction.

A Machine Learning and Data Science paper in 2020 reported Prophet yielded a 90% accuracy for 1-month Ethereum forecasts and 85% accuracy for 3-month predictions based on past data.

In a famous book on crypto investing – Cryptoassets, the authors (Burniske and Tatar) have described PROPHET as “currently one of the most accurate models” for Ethereum price forecasting.

Related Readings

• Ethereum Price Prediction Using ARIMA Model
• Ethereum Price Prediction Using LSTM
• Ethereum Price Prediction Using SVM
• Ethereum Price Prediction 2023 – 2033

Bottom Line

With new cryptocurrencies and blockchain projects increasing, the market is poised for further growth in the coming years. The dramatic ascent underscores the increasing mainstream embrace of digital assets and decentralized finance. The predictive market will undoubtedly be more competitive in the forthcoming years as forecasting the price of cryptocurrencies is a big business. Choosing the best price prediction tool doesn’t only help you save money and time in the fast-forwarding world but also assists you in fulfilling your investment goal.